FAPAR retrieval from GF-5 hyperspectral images based on unified BRDF model

نویسندگان

چکیده

æ¤è¢«å ‰åˆæœ‰æ•ˆè¾å°„å¸æ”¶æ¯”çŽ‡ï¼ˆFAPARï¼‰æ˜¯æè¿°æ¤è¢«å ‰åˆä½œç”¨èƒ½é‡äº¤æ¢è¿‡ç¨‹çš„é‡è¦å‚æ•°ï¼Œå¹¿æ³›åº”ç”¨äºŽæ¤è¢«é•¿åŠ¿ç›‘æµ‹ã€æ¤è¢«ç”Ÿäº§åŠ›ä¼°ç®—ã€å ¨çƒå˜åŒ–ç­‰ç ”ç©¶é¢†åŸŸã€‚é¥æ„Ÿæ˜¯å¤§èŒƒå›´èŽ·å–FAPARçš„å”¯ä¸€é€”å¾„ï¼Œä¸Žå¤šå ‰è°±ä¼ æ„Ÿå™¨ç›¸æ¯”ï¼Œé«˜å æ„Ÿå™¨èƒ½æ›´åŠ ç²¾ç¡®ã€ç»†è‡´åœ°è§‚æµ‹æ¤è¢«çš„å ‰è°±ç‰¹å¾ï¼Œæœ‰åˆ©äºŽåˆ†æžæ¤è¢«å† å±‚åå°„ã€å¸æ”¶ç‰¹æ€§ï¼Œè¿›è€Œåæ¼”æ¤è¢«å† å±‚FAPARã€‚æœ¬æ–‡é¦–å ˆåœ¨æ¤è¢«BRDF统一模型和FAPAR-P模型的基础上,构建了BRDF-FAPAR统一模型UBFM(Unified BRDF-FAPAR Modelï¼‰ï¼›è¿›è€ŒåŸºäºŽé«˜åˆ†äº”å·é«˜å æ„Ÿå™¨ç‰¹å¾æ¨¡æ‹Ÿäº†ä¸åŒæƒ å†µä¸‹æ¤è¢«å† å±‚åå°„çŽ‡å’Œç›¸åº”çš„FAPAR;然后运用改进的最佳指数法选择FAPAR反演的特征波段组合;在此基础上,将特征波段反射率与FAPARæ¨¡æ‹Ÿç»“æžœä½œä¸ºç¥žç»ç½‘ç»œçš„è¾“å ¥å‚æ•°ï¼Œæž„å»ºé’ˆå¯¹é«˜å ‰è°±æ•°æ®çš„FAPARç¥žç»ç½‘ç»œåæ¼”ç®—æ³•ã€‚ç ”ç©¶ç»“æžœè¡¨æ˜Žï¼Œæ”¹è¿›çš„æœ€ä½³æŒ‡æ•°æ³•èƒ½æœ‰æ•ˆåœ°ç­›é€‰å‡ºFAPARä¼°ç®—çš„æ•æ„Ÿæ³¢æ®µï¼›ç»¼åˆè€ƒè™‘æ³¢æ®µä¿¡æ¯é‡å’Œå®žé™ å½±åƒæ•°æ®å™ªå£°å½±å“ï¼Œæœ¬ç ”ç©¶é’ˆå¯¹é«˜åˆ†äº”å·é«˜å æ„Ÿå™¨é€‰æ‹©8个波段作为FAPAR反演特征波段。基于UBFM模型构建的神经网络反演精度较高,模拟实验算法误差约为0.014ã€‚é€‰æ‹©å† è’™å¤å‘¼ä¼¦è´å°”å¸‚è°¢å°”å¡”æ‹‰è‰åŽŸä¸ºä¸»è¦ç ”ç©¶åŒºï¼Œé‡‡ç”¨é«˜åˆ†äº”å·é«˜å ‰è°±å½±åƒæ•°æ®åæ¼”äº†ç ”ç©¶åŒºçš„FAPAR,并利用同步地面实测数据开展验证,反演误差为0.048ã€‚è¯¥ç®—æ³•ç®€åŒ–äº†ä¼ ç»Ÿæœºç†æ–¹æ³•çš„ä¸­é—´çŽ¯èŠ‚å’Œç¹ççš„å‚æ•°è®¾ç½®ï¼Œæœ‰è¾ƒå¥½çš„å¯è¡Œæ€§ã€ç¨³å®šæ€§å’Œç²¾åº¦ï¼Œä¸ºå›½äº§å«æ˜Ÿé«˜å æ„Ÿå™¨åœ°è¡¨æ¤è¢«å‚数定量反演提供了新途径。

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ژورنال

عنوان ژورنال: Journal of remote sensing

سال: 2023

ISSN: ['1007-4619', '2095-9494']

DOI: https://doi.org/10.11834/jrs.20210097